HDRD-Net: High-resolution detail-recovering image deraining network

Dingkun ZHU, Sen DENG, Weiming WANG, Kwok Shing CHENG, Mingqiang WEI, Fu Lee WANG, Haoran XIE

Research output: Contribution to journalArticlespeer-review

3 Citations (Scopus)


Image deraining aims to restore the clean scenes of rainy images, which facilitates a number of outdoor vision systems, such as autonomous driving, unmanned aerial vehicles and surveillance systems. This paper proposes a high-resolution detail-recovering image deraining network (HDRD-Net) to effectively remove rain streaks and recover lost details, as well as improving the quality of derained images. HDRD-Net consists of three sub-networks. First, we combine the residual network and Squeeze-and-Excitation block for rain streak removal. Second, we integrate the Structure Detail Context Aggregation block into the detail-recovering network to extract detail features form rainy images. Third, a dual super-resolution reconstruction network is utilized to enhance the quality of derained images. In addition, we extend the Rain100 dataset by incorporating low-resolution rainy images to construct a new Rain100++ dataset for high-resolution image deraining. Experimental results on several datasets show that HDRD-Net outperforms state-of-the-art methods in terms of rain removal, detail preservation and visual quality. Copyright © 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Original languageEnglish
Pages (from-to)42889-42906
JournalMultimedia Tools and Applications
Early online dateAug 2022
Publication statusPublished - Dec 2022


Zhu, D., Deng, S., Wang, W., Cheng, G., Wei, M., Wang, F. L., & Xie, H. (2022). HDRD-Net: High-resolution detail-recovering image deraining network. Multimedia Tools and Applications, 81, 42889-42906. doi: 10.1007/s11042-022-13489-5


  • HDRD-Net
  • Image deraining
  • High-resolution
  • Detail-recovering
  • Outdoor vision systems


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